Senior AI Engineer, MapGPT

Accountabilities:

• Define technical approaches for AI-powered products, establish measurement frameworks, and build evaluation systems for non-deterministic behavior.
• Develop datasets, hypotheses, evaluation criteria, and regression gates to determine whether system changes produce correct and reliable results.
• Continuously evaluate APIs, SDKs, data representations, and reference applications from the perspective of developers, agents, and end users.
• Identify issues such as incorrect parameter usage, integration anti-patterns, and other product gaps, recommend improvements, and drive fixes through to completion.
• Own MVP delivery against an agreed technical design while balancing long-term technical quality with the need to ship useful increments quickly.
• Build and maintain data pipelines and supporting tooling, including ingestion, conflation, entity resolution, quality checks, and batch and streaming processing.
• Evaluate external datasets, models, benchmarks, research, and open-source technologies, deciding when to adopt existing solutions versus building internally.
• Design feedback loops that turn product usage into data for continuous improvement and convert recurring failures into evaluation cases.
• Define the boundary between models and the tools they call, including what the model handles, what it delegates, and how context is maintained.
• Build and improve model harnesses and agent orchestration systems while addressing issues such as stale context, hallucinated arguments, partial success, and unbounded loops.
• Optimize systems against latency and cost targets through streaming, partial results, caching, model routing, and prompt design.
• Build internal developer tools and harnesses, including CLI and MCP-based tooling, and share reusable components across teams.
• Contribute to code and design reviews while mentoring engineers on evaluation practices and high-quality AI system development.
• Help investigate and solve emerging technical challenges where established approaches may not yet exist.
• Participate in an on-call rotation supporting system availability 24/7, including potential response requirements outside normal working hours and on weekends.

Requirements:

• Bachelor's degree in a STEM discipline and 5+ years of software engineering experience with production ownership of services, pipelines, SDKs, or comparable systems.
• 2+ years of experience shipping LLM-backed features to real users in production environments with customers, error budgets, and on-call responsibilities.
• Strong data engineering foundation, including SQL, at least one distributed processing framework, and experience building pipelines where data accuracy and correctness are critical.
• Strong understanding of tool calling and agent orchestration, including common failure modes such as stale context, hallucinated arguments, silent partial success, and unbounded loops.
• Strong proficiency in Python or TypeScript, with the ability to read and work with code written in other languages when necessary.
• Direct experience or deep understanding of evaluation design for non-deterministic AI systems, including the ability to explain datasets created and the failures they identified.
• Working knowledge of more than one agent harness and an understanding of the strengths and limitations of different approaches.
• Experience diagnosing latency across distributed request paths.
• Strong judgment and comfort working with ambiguity, including the ability to deliver focused solutions while broader requirements are still evolving.
• Experience with geospatial data, including routing, geocoding, points of interest, address data, OpenStreetMap, or data conflation, is a plus.
• Public API or SDK design experience, particularly for developer-facing products, is a plus.
• Experience with MCP or similar tool transports is beneficial.
• Experience running evaluations in CI, whether through commercial tooling or internally developed systems, is a plus.
• Automotive, in-vehicle infotainment, CarPlay, or Android Auto experience is beneficial.
• Voice pipeline experience involving streaming ASR, TTS, barge-in, endpointing, or wake-word systems is a plus.
• Experience working under constrained compute, offline, or intermittent connectivity conditions is valuable.
• Experience taking a product through its first external integrations and addressing gaps identified through real-world customer usage is beneficial.
• Strong collaboration, communication, mentoring, and problem-solving skills.

Benefits:

• Annual base compensation of $160,650–$217,350 for most U.S. locations, with compensation typically 5%–10% higher for U.S. locations with a higher cost of labor.
• Actual compensation determined based on qualifications, relevant skills and experience, role scope, potential impact, market conditions, and specific work location.
• Hybrid and remote work options, providing flexibility to work from home or periodically from an applicable office.
• Comprehensive private medical coverage for employees and dependents.
• Generous maternity and paternity leave.
• Fertility and family-building support.
• Lifestyle Spending Account contributions supporting health, wellness, and personal growth.
• Mental health support for employees and dependents.
• Flexible paid time away, company holidays, and generous absence policies.
• Dedicated paid volunteering time in addition to standard PTO.
• Retirement plans with competitive matching.
• Learning and development opportunities within a collaborative engineering environment.
• Opportunity to work on advanced AI systems spanning search, navigation, location intelligence, data, and agent-facing platforms.
• Exposure to complex production AI challenges involving LLMs, data pipelines, evaluation systems, and distributed architectures.